A novel clipping technique for filtering FM signals embedded in intensive noise

A novel adaptive clipping technique for filtering a constant amplitude frequency modulated (FM) signal embedded in non-Gaussian noise is proposed. It is based on the analysis and processing of the estimate of probability density function of a FM signal realization. As a result, modifications of two robust estimators of FM signal amplitude are proposed. It is shown that these estimators can be used for Gaussian and non-Gaussian heavy-tail environments. The proposed clipping technique can exploit one or another obtained robust estimate of the signal amplitude for adaptive setting a threshold. Analysis of signal estimate accuracy for different noise environments is carried out. Comparative analysis of the obtained methods and known approaches based on scanning window nonlinear filtering and optimal robust L-DFT form is performed. It is demonstrated that the usage of clipping-based technique leads to the considerable improvement of the FM signal filtering efficiency in comparison to the aforementioned known approaches for different noise environments and a wide range of input SNR values.     

A Hybrid Symbiotic Evolution–Neuro Fuzzy Strategy for Permeability Estimation of the Reservoir

A key parameter for reservoir characterization is permeability distribution. Most well log data and core permeability values are corrupted by noise (such as uncertain depth-matching, core testing conditions, and thin beddings). In this work, the authors first used wavelet as a new powerful tool for de-noising data points and then they investigated how the integration of back propagation with group based symbiotic evolution improves the reliability and prediction capability of neuro-fuzzy systems for predicting permeability of real reservoir data.     

基于矢量奇异值分解的DOA估计方法及其改进

对相干信号的波达方向（DOA）估计是空间超分辨谱估计的热点。在均匀线性阵列模型下,特征矢量奇异值分解法（ESVD）能够很好的对相干信号进行DOA估计,但是当相干信号和非相关信号同时存在时,ESVD并不能对全部信号进行DOA的估计。本文通过对ESVD算法的理论分析后,选取经过加权处理的特征向量来构造新矩阵,再利用奇异值分解得到信号的噪声和信号子空间,从而进行DOA估计。理论分析和计算机仿真表明该改进算法(MESVD)解决了ESVD算法在相干信号和不相关信号同时存在不能正确进行DOA估计的问题,估计精度与空间平滑算法（FBSS）相当。     

一种电网可疑参数诊断方法研究

电网参数错误将影响电力系统状态估计的质量,并降低能量管理系统中其他高级应用软件的实用化程度。当存在多个遥测坏数据和参数错误时,如何保证状态估计结果的有效性非常重要。提出了一种电网可疑参数诊断方法,将参数错误和量测错误在信息空间统一建模,根据加权最小二乘方法计算各量测量的百分比残差并通过阈值比较获得可疑测点集合;计算各可疑测点以及可疑支路的相关度指标,针对各可疑支路逐一采用牛顿下山法求解可疑参数的最优估计,并更新参数集。算例仿真结果表明,该方法能够综合考虑网络参数错误和量测数据错误,并且能够适用于多个错误参数并存的情况,有效地提升了参数辨识与诊断的准确度,对于提高电网运行基础数据的质量与电网高级应用的实用化程度发挥了重要作用。     

Fuzzy Logic-based Electrothermal Life Model for Inverter-fed Induction Motor Insulation

Pulse width modulated adjustable speed drives used in industries lead to significant advantages in terms of performance, size, and efficiency. But, the output voltage and the current waveforms become non-sinusoidal leading to an increase in voltage and thermal stresses. This results into accelerated insulation aging and premature failure of the motors. With multiple stresses, the life models for insulating materials become complex and ambiguous. This article presents the fuzzy logic application to derive an electrothermal life model to investigate the synergic effects of voltage and thermal stresses on intrinsic aging of inverter-fed induction motor insulation. Three parameters, viz. voltage stress factor, waveform slope stress factor, and spike frequency factor, are proposed to describe the insulation stresses for pulse width modulated voltages. These parameters are computed from the experimental results at different switching frequencies and are used in fuzzy logic-based life estimation algorithms. The results of the fuzzy logic life model are verified by performing the accelerated aging test with the same pulse width modulated voltages on paper insulation. An electrothermal life model is derived from the fuzzy logic results, which can be directly used for the life estimation of any general purpose low-voltage inverter-fed induction motor insulation.     

A Voltage Phasor Based Fault-classification Method for Phasor Measurement Unit Only State Estimator Output

Abstract—This article presents a fault-classification method for transmission lines based on voltage phasors using classification and regression trees. The proposed method is intended to aid system operators in understanding the outputs of a phasor measurement unit only state estimator. Faults are classified into four categories when the estimator is positive sequence and into ten categories when the estimator is three phase. The fault data are generated in PowerWorld® (PowerWorld Corporation, Champaign, IL, USA) and DSA Tools® (Powertech Labs Inc., Surrey, British Columbia, Canada). The pre-fault state consists of a variety of operating conditions and loading angles of faulted lines. The fault condition comprises different fault types, fault locations, fault impedances, and fault incidence angles. Fault classification is done using MATLAB® (The MathWorks, Natick, Massachusetts, USA).The approach is successfully tested on the IEEE-118 bus system. The results demonstrate that the technique developed here is effective and robust, irrespective of the pre-fault and fault conditions.     

Multi-objective Phasor Measurement Unit Placement in Electric Power Networks: Integer Linear Programming Formulation

A multi-objective optimal phasor measurement unit placement model using integer linear programming is presented in this article. The proposed model simultaneously optimizes two objectives, i.e., minimization of phasor measurement unit numbers and maximization of measurement redundancy. To calculate the redundancy criteria, the single-line outage and the phasor measurement unit loss are considered simultaneously. A linear formulation is presented for both objective functions. Also herein, to address conflicting attributes and identify Pareto optimal solutions of the multi-objective optimal phasor measurement unit placement problem, a new multi-objective mathematical programming method is proposed. Finally, a new index, i.e., minimum distance to utopia point, is implemented to select the most preferred solution among the available Pareto front based options on the goal to achieve judicious decision makers. Two test systems, i.e., a modified 9-bus and an IEEE 118-bus test systems, are used to illustrate the effectiveness of the proposed framework.     

内嵌式永磁同步电机改进型解耦控制

内嵌式永磁同步电机(IPMSM)具有好的转矩性能和宽的调速范围,但其电流控制器需要精确的参数模型,由于饱和效应影响,电感参数会随运行条件不同发生改变,控制器受耦合影响,系统容易出现速度及转矩超调、扰动等现象。为了提高IPMSM恒转矩及恒功率区的控制性能,本文设计了一种改进型电流解耦控制器。控制器基于模型参考自适应法对参数进行在线检测,检测系统加入了前馈及闭环反馈,保证了参数检测的准确性及鲁棒性。这种解耦电流控制器结构简单、紧凑,有效改善了控制性能。实验和仿真结果表明,这种改进型控制器能有效准确的实时在线检测参数,同时使得整个系统响应速度快、无超调、系统转速稳定无扰动现象,能明显地提升系统性能。     

基于非参数核密度估计的扩展准蒙特卡洛随机潮流方法

扩展蒙特卡洛方法将随机潮流的误差转化为可控量,并可在样本数增加时保留已知的潮流计算结果。基于扩展拉丁超立方抽样的方法效率比简单随机抽样高,但仍存在两方面的问题:第一,扩展拉丁超立方抽样无法保证序列的差异性,这成为计算效率提高的瓶颈;第二,当输出变量偏离正态分布时,扩展拉丁超立方抽样方法缺乏性能良好的收敛判据。针对以上两个问题,采用基于Sobol序列的扩展准蒙特卡洛方法进行随机潮流计算,并提出基于非参数核密度估计方法的收敛判据。对IEEE 30和IEEE 118节点系统的仿真结果表明,所述方法比扩展拉丁超立方抽样方法更加方便、准确,同时效率更高、收敛更快;而基于非参数核密度估计的收敛判据直观、适应性强,对变量的概率分布没有附加条件,可准确指导扩展随机潮流的收敛。